{"title":"Výuka geoinformatických předmětů na příkladech dat Evropské Unie","authors":"Zdeňka Dobešová, Karel Macků, Michal Kučera","doi":"10.31490/9788024846071-153","DOIUrl":null,"url":null,"abstract":"Data Mining and Advanced Geodata Processing are compulsory courses for the Master's degree in Geoinformatics and Cartography at Palacký University. The practical exercises use data provided by European authorities. In both courses, these are data provided by Eurostat, the statistical office of the European Union, and data provided by the European Environment Agency under Copernicus Land Monitoring Service - Urban Atlas. The benefit of the practical exercises is not only to practise different methods of analysis but also to become familiar with the sources of European Union data. Thus, the practical examples increase the knowledge of European geographical topics and the possibilities of obtaining data from freely available sources. In the Data Mining course, the topics of correlation, principal component analysis, hierarchical and non-hierarchical clustering are practiced on employment data according to the NACE Level1 economic activity code. Time series analysis is practiced on rail traffic data in EU countries. Of interest is the identification of passenger and freight traffic trends from 2005 to 2021 in each European country. Furthermore, the quarterly changes in traffic due to the covid-19 pandemic in 2020 and 2021 can be well identified from the data. Similarity search procedures are shown on Urban Atlas data. In addition, the use of trained neural networks is practiced to find the similarity of European cities according to land use centres of cities. The semester assignment is also based on European data. In the exercise, the data mining software Orange is used with a visual programming language. The Advanced Geodata Processing course focuses primarily on topics related to spatial statistics - first, it guides students through advanced exploratory analysis methods, then spatially weighted methods are introduced, followed by the use of spatial regression models. The second part of the syllabus consists of the use of geocomputation methods, in which students are introduced to the topics of fuzzy logic, information theory and fractal geometry and their applications in space. Regional NUTS2 statistics from Eurostat and OECD database are used for the exercises. Educational texts such as the Orange software textbook and exercise book for self-practice on EU data are freely available, including source data and program codes, on the project website http://urbandm.upol.cz/ in the Teaching Materials section.","PeriodicalId":186853,"journal":{"name":"GIS Ostrava 2022. Smart City – vize a realita","volume":"50 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GIS Ostrava 2022. Smart City – vize a realita","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31490/9788024846071-153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Data Mining and Advanced Geodata Processing are compulsory courses for the Master's degree in Geoinformatics and Cartography at Palacký University. The practical exercises use data provided by European authorities. In both courses, these are data provided by Eurostat, the statistical office of the European Union, and data provided by the European Environment Agency under Copernicus Land Monitoring Service - Urban Atlas. The benefit of the practical exercises is not only to practise different methods of analysis but also to become familiar with the sources of European Union data. Thus, the practical examples increase the knowledge of European geographical topics and the possibilities of obtaining data from freely available sources. In the Data Mining course, the topics of correlation, principal component analysis, hierarchical and non-hierarchical clustering are practiced on employment data according to the NACE Level1 economic activity code. Time series analysis is practiced on rail traffic data in EU countries. Of interest is the identification of passenger and freight traffic trends from 2005 to 2021 in each European country. Furthermore, the quarterly changes in traffic due to the covid-19 pandemic in 2020 and 2021 can be well identified from the data. Similarity search procedures are shown on Urban Atlas data. In addition, the use of trained neural networks is practiced to find the similarity of European cities according to land use centres of cities. The semester assignment is also based on European data. In the exercise, the data mining software Orange is used with a visual programming language. The Advanced Geodata Processing course focuses primarily on topics related to spatial statistics - first, it guides students through advanced exploratory analysis methods, then spatially weighted methods are introduced, followed by the use of spatial regression models. The second part of the syllabus consists of the use of geocomputation methods, in which students are introduced to the topics of fuzzy logic, information theory and fractal geometry and their applications in space. Regional NUTS2 statistics from Eurostat and OECD database are used for the exercises. Educational texts such as the Orange software textbook and exercise book for self-practice on EU data are freely available, including source data and program codes, on the project website http://urbandm.upol.cz/ in the Teaching Materials section.